We follow the data-format used in Google Speech Command [1]} and refine all input utterances of low-resource spoken commands to under 1 second. We use the original white noise setup in [1]} to simulate a noisy background. Mel-scaled spectra in 60 bands and 1024-point discrete Fourier transform are extracted from the input speech signals have been extracted its with Librosa [3]} and Keras audio pre-processing layer [4]}. For NISQ simulation, We run TensorFlow quantum on TPU for our experiments with 22.7% reduced decoding time compared to CPU-based simulation.
